scholarly journals Integration of Mouse and Human Single-cell RNA Sequencing Infers Spatial Cell-type Composition in Human Brains

2019 ◽  
Author(s):  
Travis S Johnson ◽  
Zachary B Abrams ◽  
Bryan R Helm ◽  
Peter Neidecker ◽  
Raghu Machiraju ◽  
...  
Keyword(s):  
Human Brain ◽  
Single Cell ◽  
Brain Tissue ◽  
Cellular Localization ◽  
Cell Types ◽  
Brain Atlas ◽  
P Value ◽  
Cell Type ◽  
Human Brain Tissue ◽  
Human Data

Technical advances have enabled the identification of high-resolution cell types within tissues based on single-cell transcriptomics. However, such analyses are restricted in human brain tissue due to the limited number of brain donors. In this study, we integrate mouse and human data to predict cell-type proportions in human brain tissue, and spatially map the resulting cellular composition. By applying feature selection and linear modeling, combinations of human and mouse brain single-cell transcriptomics profiles can be integrated to "fill in" missing information. These combined "in silico chimeric" datasets are used to model the composition of nine cell types in 3,702 human brain samples in six Allen Human Brain Atlas (AHBA) donors. Cell types were spatially consistent regardless of the scRNA-Seq dataset (91% significantly correlated) or AHBA donor (p-value = 4.43E-20 by t-test) used in the model. Importantly, neuron nuclei location and neuron mRNA location were correlated only after accounting for neural connectivity (p-value = 1.26E-10), which supports the notion that gene expression is a better indicator than nuclei location of cellular localization for cells with large and irregularly shaped cell bodies, such as neurons. These results advocate for the integration of mouse and human data in models of brain tissue heterogeneity.

scholarly journals Spatial cell type composition in normal and Alzheimers human brains is revealed using integrated mouse and human single cell RNA sequencing

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Travis S. Johnson ◽  
Shunian Xiang ◽  
Bryan R. Helm ◽  
Zachary B. Abrams ◽  
Peter Neidecker ◽  
...  
Keyword(s):  
Human Brain ◽  
Single Cell ◽  
Rna Sequencing ◽  
Brain Tissue ◽  
Cell Types ◽  
Brain Atlas ◽  
Cell Type ◽  
Cell Type Composition ◽  
Type Composition ◽  
Single Cell Rna Sequencing

Abstract Single-cell RNA sequencing (scRNA-seq) resolves heterogenous cell populations in tissues and helps to reveal single-cell level function and dynamics. In neuroscience, the rarity of brain tissue is the bottleneck for such study. Evidence shows that, mouse and human share similar cell type gene markers. We hypothesized that the scRNA-seq data of mouse brain tissue can be used to complete human data to infer cell type composition in human samples. Here, we supplement cell type information of human scRNA-seq data, with mouse. The resulted data were used to infer the spatial cellular composition of 3702 human brain samples from Allen Human Brain Atlas. We then mapped the cell types back to corresponding brain regions. Most cell types were localized to the correct regions. We also compare the mapping results to those derived from neuronal nuclei locations. They were consistent after accounting for changes in neural connectivity between regions. Furthermore, we applied this approach on Alzheimer’s brain data and successfully captured cell pattern changes in AD brains. We believe this integrative approach can solve the sample rarity issue in the neuroscience.

Localization of migraine susceptibility genes in human brain by single-cell RNA sequencing

Cephalalgia ◽  
2018 ◽  
Vol 38 (13) ◽  
pp. 1976-1983 ◽  
Author(s):  
William Renthal
Keyword(s):  
Human Brain ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Profiles ◽  
Cell Types ◽  
Susceptibility Genes ◽  
Brain Cell ◽  
Cell Type ◽  
Single Cell Rna Sequencing ◽  
Brain Cell Types

Background Migraine is a debilitating disorder characterized by severe headaches and associated neurological symptoms. A key challenge to understanding migraine has been the cellular complexity of the human brain and the multiple cell types implicated in its pathophysiology. The present study leverages recent advances in single-cell transcriptomics to localize the specific human brain cell types in which putative migraine susceptibility genes are expressed. Methods The cell-type specific expression of both familial and common migraine-associated genes was determined bioinformatically using data from 2,039 individual human brain cells across two published single-cell RNA sequencing datasets. Enrichment of migraine-associated genes was determined for each brain cell type. Results Analysis of single-brain cell RNA sequencing data from five major subtypes of cells in the human cortex (neurons, oligodendrocytes, astrocytes, microglia, and endothelial cells) indicates that over 40% of known migraine-associated genes are enriched in the expression profiles of a specific brain cell type. Further analysis of neuronal migraine-associated genes demonstrated that approximately 70% were significantly enriched in inhibitory neurons and 30% in excitatory neurons. Conclusions This study takes the next step in understanding the human brain cell types in which putative migraine susceptibility genes are expressed. Both familial and common migraine may arise from dysfunction of discrete cell types within the neurovascular unit, and localization of the affected cell type(s) in an individual patient may provide insight into to their susceptibility to migraine.

Cell Type–Specific Nuclei Markers: The Need for Human Brain Research to Go Nuclear

2021 ◽  
pp. 107385842110373
Author(s):  
James A. Wiseman ◽  
Mike Dragunow ◽  
Thomas I.-H. Park
Keyword(s):  
Human Brain ◽  
Brain Research ◽  
Cell Types ◽  
Brain Cell ◽  
Normal Brain ◽  
Cell Type ◽  
Human Brain Tissue ◽  
Nuclear Markers ◽  
Cell Type Specific ◽  
Immunohistochemical Studies

Identifying and interrogating cell type–specific populations within the heterogeneous milieu of the human brain is paramount to resolving the processes of normal brain homeostasis and the pathogenesis of neurological disorders. While brain cell type–specific markers are well established, most are localized on cellular membranes or within the cytoplasm, with limited literature describing those found in the nucleus. Due to the complex cytoarchitecture of the human brain, immunohistochemical studies require well-defined cell-specific nuclear markers for more precise and efficient quantification of the cellular populations. Furthermore, efficient nuclear markers are required for cell type–specific purification and transcriptomic interrogation of archived human brain tissue through nuclei isolation–based RNA sequencing. To sate the growing demand for robust cell type–specific nuclear markers, we thought it prudent to comprehensively review the current literature to identify and consolidate a novel series of robust cell type–specific nuclear markers that can assist researchers across a range of neuroscientific disciplines. The following review article collates and discusses several key and prospective cell type–specific nuclei markers for each of the major human brain cell types; it then concludes by discussing the potential applications of cell type–specific nuclear workflows and the power of nuclear-based neuroscientific research.

scholarly journals FR-Match: Robust matching of cell type clusters from single cell RNA sequencing data using the Friedman-Rafsky non-parametric test

2020 ◽  
Author(s):  
Yun Zhang ◽  
Brian D. Aevermann ◽  
Trygve E. Bakken ◽  
Jeremy A. Miller ◽  
Rebecca D. Hodge ◽  
...  
Keyword(s):  
Human Brain ◽  
Single Cell ◽  
Rna Sequencing ◽  
Cell Types ◽  
R Package ◽  
Brain Regions ◽  
Cortical Layer ◽  
Middle Temporal Gyrus ◽  
Cell Type ◽  
Sequencing Data

AbstractSingle cell/nucleus RNA sequencing (scRNAseq) is emerging as an essential tool to unravel the phenotypic heterogeneity of cells in complex biological systems. While computational methods for scRNAseq cell type clustering have advanced, the ability to integrate datasets to identify common and novel cell types across experiments remains a challenge. Here, we introduce a cluster-to-cluster cell type matching method – FR-Match – that utilizes supervised feature selection for dimensionality reduction and incorporates shared information among cells to determine whether two cell type clusters share the same underlying multivariate gene expression distribution. FR-Match is benchmarked with existing cell-to-cell and cell-to-cluster cell type matching methods using both simulated and real scRNAseq data. FR-Match proved to be a stringent method that produced fewer erroneous matches of distinct cell subtypes and had the unique ability to identify novel cell phenotypes in new datasets. In silico validation demonstrated that the proposed workflow is the only self-contained algorithm that was robust to increasing numbers of true negatives (i.e. non-represented cell types). FR-Match was applied to two human brain scRNAseq datasets sampled from cortical layer 1 and full thickness middle temporal gyrus. When mapping cell types identified in specimens isolated from these overlapping human brain regions, FR-Match precisely recapitulated the laminar characteristics of matched cell type clusters, reflecting their distinct neuroanatomical distributions. An R package and Shiny application are provided at https://github.com/JCVenterInstitute/FRmatch for users to interactively explore and match scRNAseq cell type clusters with complementary visualization tools.

scholarly journals Parallel RNA and DNA analysis after deep sequencing (PRDD-seq) reveals cell type-specific lineage patterns in human brain

2020 ◽  
Vol 117 (25) ◽  
pp. 13886-13895 ◽  
Author(s):  
August Yue Huang ◽  
Pengpeng Li ◽  
Rachel E. Rodin ◽  
Sonia N. Kim ◽  
Yanmei Dou ◽  
...  
Keyword(s):  
Human Brain ◽  
Single Cell ◽  
Deep Sequencing ◽  
Dna Analysis ◽  
Cell Lineage ◽  
Neuronal Cell ◽  
Cell Types ◽  
Inhibitory Neurons ◽  
Cell Type ◽  
Rna And Dna

Elucidating the lineage relationships among different cell types is key to understanding human brain development. Here we developed parallel RNA and DNA analysis after deep sequencing (PRDD-seq), which combines RNA analysis of neuronal cell types with analysis of nested spontaneous DNA somatic mutations as cell lineage markers, identified from joint analysis of single-cell and bulk DNA sequencing by single-cell MosaicHunter (scMH). PRDD-seq enables simultaneous reconstruction of neuronal cell type, cell lineage, and sequential neuronal formation (“birthdate”) in postmortem human cerebral cortex. Analysis of two human brains showed remarkable quantitative details that relate mutation mosaic frequency to clonal patterns, confirming an early divergence of precursors for excitatory and inhibitory neurons, and an “inside-out” layer formation of excitatory neurons as seen in other species. In addition our analysis allows an estimate of excitatory neuron-restricted precursors (about 10) that generate the excitatory neurons within a cortical column. Inhibitory neurons showed complex, subtype-specific patterns of neurogenesis, including some patterns of development conserved relative to mouse, but also some aspects of primate cortical interneuron development not seen in mouse. PRDD-seq can be broadly applied to characterize cell identity and lineage from diverse archival samples with single-cell resolution and in potentially any developmental or disease condition.

scholarly journals Measuring cell-type specific differential methylation in human brain tissue

2013 ◽  
Vol 14 (8) ◽  
pp. R94 ◽  
Author(s):  
Carolina M Montaño ◽  
Rafael A Irizarry ◽  
Walter E Kaufmann ◽  
Konrad Talbot ◽  
Raquel E Gur ◽  
...  
Keyword(s):  
Human Brain ◽  
Brain Tissue ◽  
Differential Methylation ◽  
Cell Type ◽  
Human Brain Tissue ◽  
Measuring Cell ◽  
Cell Type Specific

scholarly journals FR-Match: robust matching of cell type clusters from single cell RNA sequencing data using the Friedman–Rafsky non-parametric test

2020 ◽  
Author(s):  
Yun Zhang ◽  
Brian D Aevermann ◽  
Trygve E Bakken ◽  
Jeremy A Miller ◽  
Rebecca D Hodge ◽  
...  
Keyword(s):  
Human Brain ◽  
Single Cell ◽  
Rna Sequencing ◽  
Cell Types ◽  
R Package ◽  
Brain Regions ◽  
Cortical Layer ◽  
Middle Temporal Gyrus ◽  
Cell Type ◽  
Sequencing Data

Abstract Single cell/nucleus RNA sequencing (scRNAseq) is emerging as an essential tool to unravel the phenotypic heterogeneity of cells in complex biological systems. While computational methods for scRNAseq cell type clustering have advanced, the ability to integrate datasets to identify common and novel cell types across experiments remains a challenge. Here, we introduce a cluster-to-cluster cell type matching method—FR-Match—that utilizes supervised feature selection for dimensionality reduction and incorporates shared information among cells to determine whether two cell type clusters share the same underlying multivariate gene expression distribution. FR-Match is benchmarked with existing cell-to-cell and cell-to-cluster cell type matching methods using both simulated and real scRNAseq data. FR-Match proved to be a stringent method that produced fewer erroneous matches of distinct cell subtypes and had the unique ability to identify novel cell phenotypes in new datasets. In silico validation demonstrated that the proposed workflow is the only self-contained algorithm that was robust to increasing numbers of true negatives (i.e. non-represented cell types). FR-Match was applied to two human brain scRNAseq datasets sampled from cortical layer 1 and full thickness middle temporal gyrus. When mapping cell types identified in specimens isolated from these overlapping human brain regions, FR-Match precisely recapitulated the laminar characteristics of matched cell type clusters, reflecting their distinct neuroanatomical distributions. An R package and Shiny application are provided at https://github.com/JCVenterInstitute/FRmatch for users to interactively explore and match scRNAseq cell type clusters with complementary visualization tools.

scholarly journals Parallel RNA and DNA analysis after Deep-sequencing (PRDD-seq) reveals cell type specific lineage patterns in human brain

2020 ◽  
Author(s):  
August Yue Huang ◽  
Pengpeng Li ◽  
Rachel E. Rodin ◽  
Sonia N. Kim ◽  
Yanmei Dou ◽  
...  
Keyword(s):  
Human Brain ◽  
Single Cell ◽  
Cell Lineage ◽  
Neuronal Cell ◽  
Cell Types ◽  
Inhibitory Neurons ◽  
Cell Type ◽  
Cell Divisions ◽  
Excitatory Neurons ◽  
The Brain

AbstractElucidating the lineage relationships among different cell types is key to understanding human brain development. Here we developed Parallel RNA and DNA analysis after Deep-sequencing (PRDD-seq), which combines RNA analysis of neuronal cell types with analysis of nested spontaneous DNA somatic mutations as cell lineage markers, identified from joint analysis of single cell and bulk DNA sequencing by single-cell MosaicHunter (scMH). PRDD-seq enables the first-ever simultaneous reconstruction of neuronal cell type, cell lineage, and sequential neuronal formation (“birthdate”) in postmortem human cerebral cortex. Analysis of two human brains showed remarkable quantitative details that relate mutation mosaic frequency to clonal patterns, confirming an early divergence of precursors for excitatory and inhibitory neurons, and an “inside-out” layer formation of excitatory neurons as seen in other species. In addition our analysis allows the first estimate of excitatory neuron-restricted precursors (about 10) that generate the excitatory neurons within a cortical column. Inhibitory neurons showed complex, subtype-specific patterns of neurogenesis, including some patterns of development conserved relative to mouse, but also some aspects of primate cortical interneuron development not seen in mouse. PRDD-seq can be broadly applied to characterize cell identity and lineage from diverse archival samples with single-cell resolution and in potentially any developmental or disease condition.Significance StatementStem cells and progenitors undergo a series of cell divisions to generate the neurons of the brain, and understanding this sequence is critical to studying the mechanisms that control cell division and migration in developing brain. Mutations that occur as cells divide are known as the basis of cancer, but have more recently been shown to occur with normal cell divisions, creating a permanent, forensic map of the clonal patterns that define the brain. Here we develop new technology to analyze both DNA mutations and RNA gene expression patterns in single cells from human postmortem brain, allowing us to define clonal patterns among different types of human brain neurons, gaining the first direct insight into how they form.

scholarly journals Single cell epigenomic atlas of the developing human brain and organoids

2019 ◽  
Author(s):  
Ryan S. Ziffra ◽  
Chang N. Kim ◽  
Amy Wilfert ◽  
Tychele N. Turner ◽  
Maximilian Haeussler ◽  
...  
Keyword(s):  
Human Brain ◽  
Single Cell ◽  
Cell Fate ◽  
Cell Types ◽  
Regulatory Elements ◽  
Chromatin Accessibility ◽  
Chromatin State ◽  
Cell Type ◽  
Fate Specification ◽  
Cell Type Specific

AbstractDynamic changes in chromatin accessibility coincide with important aspects of neuronal differentiation, such as fate specification and arealization and confer cell type-specific associations to neurodevelopmental disorders. However, studies of the epigenomic landscape of the developing human brain have yet to be performed at single-cell resolution. Here, we profiled chromatin accessibility of >75,000 cells from eight distinct areas of developing human forebrain using single cell ATAC-seq (scATACseq). We identified thousands of loci that undergo extensive cell type-specific changes in accessibility during corticogenesis. Chromatin state profiling also reveals novel distinctions between neural progenitor cells from different cortical areas not seen in transcriptomic profiles and suggests a role for retinoic acid signaling in cortical arealization. Comparison of the cell type-specific chromatin landscape of cerebral organoids to primary developing cortex found that organoids establish broad cell type-specific enhancer accessibility patterns similar to the developing cortex, but lack many putative regulatory elements identified in homologous primary cell types. Together, our results reveal the important contribution of chromatin state to the emerging patterns of cell type diversity and cell fate specification and provide a blueprint for evaluating the fidelity and robustness of cerebral organoids as a model for cortical development.

scholarly journals Evolution of regulatory signatures in primate cortical neurons at cell type resolution

2020 ◽  
Author(s):  
Alexey Kozlenkov ◽  
Marit W. Vermunt ◽  
Pasha Apontes ◽  
Junhao Li ◽  
Ke Hao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cerebral Cortex ◽  
Human Brain ◽  
Brain Tissue ◽  
Cell Types ◽  
Regulatory Elements ◽  
Autism Spectrum ◽  
Evolutionary Divergence ◽  
Cell Type ◽  
Cell Type Specific

ABSTRACTThe human cerebral cortex contains many cell types that likely underwent independent functional changes during evolution. However, cell type-specific regulatory landscapes in the cortex remain largely unexplored. Here we report epigenomic and transcriptomic analyses of the two main cortical neuronal subtypes, glutamatergic projection neurons and GABAergic interneurons, in human, chimpanzee and rhesus macaque. Using genome-wide profiling of the H3K27ac histone modification, we identify neuron-subtype-specific regulatory elements that previously went undetected in bulk brain tissue samples. Human-specific regulatory changes are uncovered in multiple genes, including those associated with language, autism spectrum disorder and drug addiction. We observe preferential evolutionary divergence in neuron-subtype-specific regulatory elements and show that a substantial fraction of pan-neuronal regulatory elements undergo subtype-specific evolutionary changes. This study sheds light on the interplay between regulatory evolution and cell-type-dependent gene expression programs, and provides a resource for further exploration of human brain evolution and function.SIGNIFICANCEThe cerebral cortex of the human brain is a highly complex, heterogeneous tissue that contains many cell types which are exquisitely regulated at the level of gene expression by non-coding regulatory elements, presumably, in a cell-type-dependent manner. However, assessing the regulatory elements in individual cell types is technically challenging, and therefore, most of the previous studies on gene regulation were performed with bulk brain tissue. Here we analyze two major types of neurons isolated from the cerebral cortex of humans, chimpanzees and rhesus macaques, and report complex patterns of cell-type-specific evolution of the regulatory elements in numerous genes. Many genes with evolving regulation are implicated in language abilities as well as psychiatric disorders.

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